Efficient deep neural network for photo-realistic image super-resolution

@article{Ahn2022EfficientDN,
  title={Efficient deep neural network for photo-realistic image super-resolution},
  author={Namhyuk Ahn and Byungkon Kang and Kyung-ah Sohn},
  journal={Pattern Recognit.},
  year={2022},
  volume={127},
  pages={108649}
}

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References

SHOWING 1-10 OF 84 REFERENCES

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

TLDR
SRGAN, a generative adversarial network (GAN) for image super-resolution (SR), is presented, to its knowledge, the first framework capable of inferring photo-realistic natural images for 4x upscaling factors and a perceptual loss function which consists of an adversarial loss and a content loss.

A Fully Progressive Approach to Single-Image Super-Resolution

TLDR
To obtain more photorealistic results, a generative adversarial network (GAN) is designed, named ProGanSR, that follows the same progressive multi-scale design principle and constitutes a principled multi- scale approach that increases the reconstruction quality for all upsampling factors simultaneously.

Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution

TLDR
This paper proposes the Laplacian Pyramid Super-Resolution Network (LapSRN) to progressively reconstruct the sub-band residuals of high-resolution images and generates multi-scale predictions in one feed-forward pass through the progressive reconstruction, thereby facilitates resource-aware applications.

EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis

TLDR
This work proposes a novel application of automated texture synthesis in combination with a perceptual loss focusing on creating realistic textures rather than optimizing for a pixelaccurate reproduction of ground truth images during training to achieve a significant boost in image quality at high magnification ratios.

Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networks

TLDR
This paper proposes the deep Laplacian Pyramid Super-Resolution Network for fast and accurate image super-resolution, and utilizes the recursive layers to share parameters across as well as within pyramid levels, and thus drastically reduce the number of parameters.

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

TLDR
This work thoroughly study three key components of SRGAN – network architecture, adversarial loss and perceptual loss, and improves each of them to derive an Enhanced SRGAN (ESRGAN), which achieves consistently better visual quality with more realistic and natural textures than SRGAN.

Image Super-Resolution Using Dense Skip Connections

TLDR
A novel single-image super-resolution method is presented by introducing dense skip connections in a very deep network, providing an effective way to combine the low-level features and high- level features to boost the reconstruction performance.

Accelerating the Super-Resolution Convolutional Neural Network

TLDR
This paper aims at accelerating the current SRCNN, and proposes a compact hourglass-shape CNN structure for faster and better SR, and presents the parameter settings that can achieve real-time performance on a generic CPU while still maintaining good performance.

Image Super-Resolution via Deep Recursive Residual Network

TLDR
This paper proposes a very deep CNN model (up to 52 convolutional layers) named Deep Recursive Residual Network (DRRN) that strives for deep yet concise networks, and recursive learning is used to control the model parameters while increasing the depth.
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